•A multiagent framework was established to simulate P2P energy sharing.•Indexes were proposed to evaluate P2P energy sharing mechanisms.•Heuristic techniques were devised to facilitate convergence of ...simulation.•Three existing P2P mechanisms were evaluated in Great Britain context.
Peer-to-peer (P2P) energy sharing involves novel technologies and business models at the demand-side of power systems, which is able to manage the increasing connection of distributed energy resources (DERs). In P2P energy sharing, prosumers directly trade energy with each other to achieve a win-win outcome. From the perspectives of power systems, P2P energy sharing has the potential to facilitate local energy balance and self-sufficiency. A systematic index system was developed to evaluate the performance of various P2P energy sharing mechanisms based on a multiagent-based simulation framework. The simulation framework is composed of three types of agents and three corresponding models. Two techniques, i.e. step length control and learning process involvement, and a last-defence mechanism were proposed to facilitate the convergence of simulation and deal with the divergence. The evaluation indexes include three economic indexes, i.e. value tapping, participation willing and equality, and three technical indexes, i.e. energy balance, power flatness and self-sufficiency. They are normalised and further synthesized to reflect the overall performance. The proposed methods were applied to simulate and evaluate three existing P2P energy sharing mechanisms, i.e. the supply and demand ratio (SDR), mid-market rate (MMR) and bill sharing (BS), for residential customers in current and future scenarios of Great Britain. Simulation results showed that both of the step length control and learning process involvement techniques improve the performance of P2P energy sharing mechanisms with moderate ramping/learning rates. The results also showed that P2P energy sharing has the potential to bring both economic and technical benefits for Great Britain. In terms of the overall performance, the SDR mechanism outperforms all the other mechanisms, and the MMR mechanism has good performance when with moderate PV penetration levels. The BS mechanism performs at the similar level as the conventional paradigm. The conclusion on the mechanism performance is not sensitive to season factors, day types and retail price schemes.
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•A trading framework is designed enabling the exchange of energy and carbon allowance.•Smart contract is exploited to automate standardised auction procedure.•The bidding/selling ...prices directly target on reshaping prosumption behaviours.•Results prove that the proposed framework facilitates regional energy balance and carbon saving.
Prosumers are active participants in future energy systems who produce and consume energy. However, the emerging role of prosumers brings challenges of tracing carbon emissions behaviours and formulating pricing scheme targeting on individual prosumption behaviours. This paper proposes a novel blockchain-based peer-to-peer trading framework to trade energy and carbon allowance. The bidding/selling prices of prosumers can directly incentivise the reshaping of prosumption behaviours to achieve regional energy balance and carbon emissions mitigation. A decentralised low carbon incentive mechanism is formulated targeting on specific prosumption behaviours. Case studies using the modified IEEE 37-bus test feeder show that the proposed trading framework can export 0.99 kWh of daily energy and save 1465.90 g daily carbon emissions, outperforming the existing centralised trading and aggregator-based trading.
Significant progress has been made in recent years in theoretical modeling of the electric double layer (EDL), a key concept in electrochemistry important for energy storage, electrocatalysis, and ...multitudes of other technological applications. However, major challenges remain in understanding the microscopic details of the electrochemical interface and charging mechanisms under realistic conditions. This review delves into theoretical methods to describe the equilibrium and dynamic responses of the EDL structure and capacitance for electrochemical systems commonly deployed for capacitive energy storage. Special emphasis is given to recent advances that intend to capture the nonclassical EDL behavior such as oscillatory ion distributions, polarization of nonmetallic electrodes, charge transfer, and various forms of phase transitions in the micropores of electrodes interfacing with an organic electrolyte or ionic liquid. This comprehensive analysis highlights theoretical insights into predictable relationships between materials characteristics and electrochemical performance and offers a perspective on opportunities for further development toward rational design and optimization of electrochemical systems.
Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The ...Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.
•This article presents some perspectives on possible engineering developments to accelerate the paradigm shift from fossil fuels to renewable energy.•The global energy demand and greenhouse-gas ...emissions mandate alternative industrial processes suitable for large scale energy conversion and chemical transformation.•Major challenges exist for a comprehensive description of energy conversion and chemical transformation in fossil-free chemical processes.
Chemical engineering is a broad field in terms of the scope of practice but the discipline has been united by a few intellectually coherent principles. Among them, thermodynamics, reaction kinetics and transport phenomena are often considered as the cornerstones, providing support for the design and operation of diverse chemical processes for power generation and production of industrial goods such as plastics, gasoline and ammonia. Traditionally, these industrial processes use fossil fuels as the raw materials and are responsible for significant greenhouse gas emissions. As fossil-energy-based processes are deemed phasing out , development of alternative routes with renewable energy and sustainable feedstock is calling for the expansion of the knowledge base such that eco-friendly chemical processes can be quantified, controlled and optimized with high precision. This article offers some perspectives on possible engineering developments to accelerate the paradigm shift from fossil fuels to renewable energy.
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A closed-loop robust distribution state estimator was investigated. An approach that is suitable for medium voltage distribution networks which are either under-determined with limited real-time ...measurements or over-determined but with delayed information from smart meters was developed. The state estimator was designed to be robust against the effect of measurement errors, the type, location and accuracy of measurements, as well as temporary failure of the smart metering communication system. A machine learning function provides reliable input information to a robust state estimation algorithm. The output of the state estimator is then fed back to the machine learning function creating a closed-loop information flow which improves the performance of the state estimator. Test results and analysis on a 33-node system are provided.
Power-to-gas (PtG) converts electricity into hydrogen using the electrolysis process and uses the gas grid for the storage and transport of hydrogen. Hydrogen is injected into a gas network in a ...quantity and quality compatible with the gas safety regulations and thereby transported as a mixture of hydrogen and natural gas to demand centres. Once integrated into the electricity network, PtG systems can provide flexibility to the power system and absorb excess electricity from renewables to produce hydrogen. Injection of hydrogen into the gas network reduces gas volumes supplied from terminals.
In order to investigate this concept, hydrogen electrolysers were included as a technology option within an operational optimisation model of the Great Britain (GB) combined gas and electricity network (CGEN). The model was used to determine the minimum cost of meeting the electricity and gas demand in a typical low and high electricity demand day in GB, in the presence of a significant capacity of wind generation. The value of employing power-to-gas systems in the gas and electricity supply system was investigated given different allowable levels of hydrogen injections. The results showed that producing hydrogen from electricity is capable of reducing wind curtailment in a high wind case and decreasing the overall cost of operating the GB gas and electricity network. The northern part of GB was identified as a suitable region to develop hydrogen electrolysis and injection facilities due to its vicinity to a significant capacity of wind generation, as well as the existence of gas network headroom capacity, which is expected to increase as a result of depletion of UK domestic gas resources.
•Role of power-to-gas (PtG) in the providing flexibility in the Great Britain electricity and gas networks is investigated.•A combined gas and electricity network optimisation model (CGEN) was used.•Employing PtG system was shown to significantly reduces wind power curtailment, and operating costs of the integrated system.
As a promising solution to address the “energy trilemma” confronting human society, peer-to-peer (P2P) energy trading has emerged and rapidly developed in recent years. When carrying out P2P energy ...trading, customers with distributed energy resources (DERs) are able to directly trade and share energy with each other. This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers, research projects, and industrial practice. Key aspects in P2P energy trading are identified and discussed, including market design, trading platforms, physical infrastructure and information and communication technology (ICT) infrastructure, social science perspectives, and policy. For each key aspect, existing research and practice are critically reviewed and insights for future development are presented. Comprehensive concluding remarks are provided at the end, summarizing the major findings and perspectives of this paper. P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.
A Monte Carlo model of the combined GB gas and electricity network was developed to determine the reliability of the energy infrastructure. The model integrates the gas and electricity network into a ...single sequential Monte Carlo simulation. The model minimises the combined costs of the gas and electricity network, these include gas supplies, gas storage operation and electricity generation. The Monte Carlo model calculates reliability indices such as loss of load probability and expected energy unserved for the combined gas and electricity network. The intention of this tool is to facilitate reliability analysis of integrated energy systems. Applications of this tool are demonstrated through a case study that quantifies the impact on the reliability of the GB gas and electricity network given uncertainties such as wind variability, gas supply availability and outages to energy infrastructure assets. Analysis is performed over a typical midwinter week on a hypothesised GB gas and electricity network in 2020 that meets European renewable energy targets. The efficacy of doubling GB gas storage capacity on the reliability of the energy system is assessed. The results highlight the value of greater gas storage facilities in enhancing the reliability of the GB energy system given various energy uncertainties.
•A Monte Carlo model of the combined GB gas and electricity network was developed.•Reliability indices are calculated for the combined GB gas and electricity system.•The efficacy of doubling GB gas storage capacity on reliability of the energy system is assessed.•Integrated reliability indices could be used to assess the impact of investment in energy assets.
A combined gas and electricity network expansion planning model was developed. Gas-fired generation plants were considered as linkages between the two networks. The model simultaneously minimises gas ...and electricity operational and network expansion costs. Additionally it optimally places planned power generation plants around the electricity network. Network expansion was implemented by adding new assets such as pipes, compressors, and storage facilities in the gas network and increasing transmission line capacity in the electricity network. The developed model was used to analyse the GB gas and electricity infrastructure expansion requirements to achieve a low carbon energy system. Two scenarios were implemented, a reference and a low carbon scenario. For both scenarios, CGEN defined a network at lowest cost capable of meeting varying demand and power generation capacity profiles. Greater peak gas demand of approximately 25mcm/d by 2030 in the reference scenario resulted in CGEN adding an additional 1Bcm of gas storage capacity compared with the low carbon scenario. LNG gas supplies were shown to account for over 70% of total gas supplies by the end of the time horizon for both scenarios. The combined gas and electricity network planning approach allows analysis into the interactions between these two networks. This interaction allows variables such as total gas supply (gas used for electricity production is an endogenous variable), which 13% higher in the reference scenario and geographic location of gas fired generation to explicitly take account of the impact on both gas and electricity infrastructures.
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